Methods and apparatus for identifying objects depicted in a video using extracted video frames in combination with a reverse image search engine

ABSTRACT

Methods and apparatus are disclosed for identifying one or more objects (e.g., a logo, brand or commercial) depicted in a video. Textual information is identified from search results pages returned by a reverse image search engine for images extracted from the video. Base query records are generated corresponding to the search results pages that have textual information satisfying a base search term. Object query records are generated corresponding to the base query records that satisfy an object search term. A statistical criterion is applied to the object query records to identify an object depicted in the video. In some disclosed examples, the statistical criterion includes a threshold that is measured against the object query records and/or the base query records.

FIELD OF THE DISCLOSURE

This disclosure relates generally to object identification techniques,and, more particularly, to methods and apparatus for identifying one ormore objects depicted in a video using extracted video frames incombination with a reverse image search engine.

BACKGROUND

Image recognition is the process of identifying and/or detecting anobject or a feature in a digital image or video. Image recognitiontechnologies may be used to identify objects, people, places, logos,brands, and/or anything else that may have value to and/or be ofinterest to individuals, consumers and/or corporate entities, etc.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is an illustration of an example computing environment in whichan example video object identification system and an example reverseimage search engine may be used to identify one or more objects depictedin a video in accordance with the teachings of this disclosure.

FIG. 2 is a block diagram of the example video object identificationsystem and the example reverse image search engine of FIG. 1.

FIG. 3 further illustrates operation of the example video frame samplerof FIG. 2.

FIG. 4 illustrates an example image generated by the example video framesampler of FIGS. 2 and/or 3.

FIG. 5 further illustrates operation of the example reverse image searchengine of FIGS. 1 and/or 2.

FIG. 6 further illustrates operation of the example screen capturer ofFIG. 2.

FIG. 7 illustrates an example screen shot generated by the examplescreen capturer of FIGS. 2 and/or 6.

FIG. 8 further illustrates operation of the example text identifier ofFIG. 2.

FIG. 9 illustrates an example identified text data structure includingexample identified textual information generated by the example textidentifier of FIGS. 2 and/or 8.

FIG. 10 further illustrates operation of the example base query recordgenerator of FIG. 2.

FIG. 11 illustrates an example base query record data structureincluding example base query records generated by the example base queryrecord generator of FIGS. 2 and/or 10.

FIG. 12 further illustrates operation of the example object query recordgenerator of FIG. 2.

FIG. 13 illustrates an example object query record data structureincluding example object query records generated by the example objectquery record generator of FIGS. 2 and/or 12.

FIG. 14 further illustrates operation of the example object identifierof FIG. 2.

FIG. 15 illustrates an example script including example machine-readableinstructions that may be executed to implement the example video objectidentification system of FIGS. 1 and/or 2.

FIG. 16 is a flowchart representative of example machine-readableinstructions that may be executed to implement the example video objectidentification system of FIGS. 1 and/or 2.

FIG. 17 is a block diagram of an example processing platform structuredto execute the example script of FIG. 15 and/or the example instructionsof FIG. 16 to implement the example video object identification systemof FIGS. 1 and/or 2.

The figures are not to scale. Wherever possible, the same referencenumbers will be used throughout the drawing(s) and accompanying writtendescription to refer to the same or like parts.

DETAILED DESCRIPTION

Consumer measurement companies spend substantial resources developingand implementing elaborate image recognition techniques and/or solutionsto identify logos, brands and/or commercials that are depicted invarious media formats. The image recognition techniques and/or solutionsdisclosed herein provide a cost-effective alternative or compliment toexisting image recognition techniques and/or solutions.

Example methods, systems, and apparatus disclosed herein identify anobject depicted in a video utilizing images extracted from the video incombination with a reverse image search engine. In some disclosedexamples, the images extracted from the video are provided to thereverse image search engine. In some disclosed examples, the images areextracted from the video by executing a video frame extractionapplication. In some disclosed examples, the reverse image search engineis hosted by a webserver.

In some disclosed examples, a search results page returned by thereverse image search engine is accessed, and textual informationpresented in the search results page is identified. In some disclosedexamples, identifying the textual information includes capturing ascreen shot of the search results page. In some disclosed examples,capturing the screen shot includes executing a screen captureapplication. In some disclosed examples, identifying the textualinformation includes executing an optical character recognitionapplication to determine the textual information from the capturedscreen shot. By obtaining the textual information from the capturedscreen shot, as opposed to obtaining the textual information directlyfrom the search results page, at least some example methods, systems andapparatus disclosed herein avoid the need to parse dynamic data (e.g.,data that can change at any time, such as, for example, HyperText MarkupLanguage (HTML), Extensible Markup Language (XML), JavaScript ObjectNotation (JSON) data, etc.) from the identified textual information.

In some disclosed examples, an object depicted in the video isidentified based on a statistical criterion applied to object queryrecords generated in response to a query of base query records, wherethe base query records are generated in response to a query of theidentified textual information. In some disclosed examples, the querythat results in the generation of the base query records includes a basesearch term, while the query that results in the generation of theobject query records includes an object search term. In some disclosedexamples, the statistical criterion includes a threshold that ismeasured against the object query records and/or the base query records(e.g., a specified total number of object query records associated witha particular object search term, a specified number of object queryrecords associated with a particular object search term within aspecified time period, a specified consecutive number of object queryrecords associated with a particular object search term, a specifiedpercentage of the number of object query records associated with aparticular object search term relative to the number of base queryrecords from which the object query records were derived, etc.). In somedisclosed examples, the identified object depicted in the video includesat least one of a logo, a brand, or a commercial.

FIG. 1 illustrates an example computing environment in which an examplevideo object identification system 100 and an example reverse imagesearch engine 130 constructed in accordance with the teachings of thisdisclosure may be used to identify one or more objects depicted in avideo. In the illustrated example, the video object identificationsystem 100 communicates with the reverse image search engine 130 via anexample network 120 which may be implemented by any number and/ortype(s) of communication networks. In some examples, the network 120 isthe Internet. In some examples, the network 120 may be implemented by aWide Area Network (WAN) or a Local Area Network (LAN), or a combinationthereof.

In the illustrated example of FIG. 1, the example video objectidentification system 100 and/or the example reverse image search engine130 can be implemented by, for example, one or more of a server, apersonal computer, a tablet, a smartphone, and/or any other type ofcomputing device. In some examples, the reverse image search engine 130is hosted by a remote webserver that is accessible to the example videoobject identification system 100 via the example network 120. While theillustrated example of FIG. 1 shows the example reverse image searchengine 130 being located remotely from the example video objectidentification system 100, the reverse image search engine 130 mayalternatively reside locally within the video object identificationsystem 100. In such an alternate example, the video objectidentification system 100 may be able to directly communicate with theexample reverse image search engine 130 without using the examplenetwork 120.

FIG. 2 is a block diagram of the example video object identificationsystem 100 and the example reverse image search engine 130 of FIG. 1constructed in accordance with the teachings of this disclosure toidentify one or more objects in a video. The example video objectidentification system 100 and/or the example reverse image search engine130, including any components and/or subsystems thereof, may beimplemented using either a single computing system or multiple computingsystems, and may be implemented using either a centralized computerarchitecture or a distributed computer architecture, or a combinationthereof.

In the illustrated example of FIG. 2, the video object identificationsystem 100 includes an example video repository 202, an example videoframe sampler 204, an example image repository 206, an example searchresults page repository 208, an example screen capturer 210, an examplescreen shot repository 212, an example text identifier 214, an exampleidentified text repository 216, an example base query record generator218, an example base query record repository 220, an example objectquery record generator 222, an example object query record repository224, and an example object identifier 226. However, other exampleimplementations of the video object identification system 100 mayinclude fewer or additional structures to carry out identification ofobjects in video in accordance with the teachings of this disclosure.

In the illustrated example of FIG. 2, the example video repository 202stores one or more videos. The example video repository 202 of FIG. 2 isimplemented by one or more hard disk drives. However, the example videorepository 202 could additionally or alternatively be implemented by anytype(s) and/or any number(s) of a storage drive, a storage disk, a flashmemory, a read-only memory (ROM), a compact disk (CD), a digitalversatile disk (DVD), a Blu-ray disc, a cache, a random-access memory(RAM) and/or any other storage medium in which information is stored forany duration (e.g., for extended time periods, permanently, briefinstances, for temporarily buffering, and/or for caching of theinformation). Videos stored in the example video repository 202 may bestored in any file and/or data structure format, organization scheme,and/or arrangement. For example, a video may be stored in the examplevideo repository 202 as an Advanced Streaming Format (.ASF), AdvancedStream Redirector (.ASX), Audio Video Interleaved (.AVI), Blu-ray DiscMovie (.BDMV), DivXNetworks (.DIVX), Flash MP4 Video (.F4V), Flash LiveVideo (.FLV), Google Video (.GVI), Matroska Multimedia Container (.MKV),Moving Picture Experts Group (.MPEG, .MPG, .MP4 or .M4V), MPEG TransportStream (.MTS or .M2TS), NullSoft Video (.NSV), QuickTime Content (.QT or.MOV), Real Media (.RM), Samsung Video (.SVI), Windows Media Video(.WMV), 3rd Generation Partnership Project (.3GP and/or .3GPP), and/or3rd Generation Partnership Project 2 (.3G2 or .3GP2) file. As describedin detail herein, videos stored in the example video repository 202 areaccessible to the example frame sampler 204 of FIG. 2, and/or, moregenerally, to the example video object identification system 100 of FIG.1.

In the illustrated example of FIG. 2, the example frame sampler 204extracts and/or samples frames and/or images from a video. In someexamples, the frame sampler 204 receives and/or otherwise retrieves thevideo from the example video repository 202 described above. The exampleframe sampler 204 of FIG. 2 is further illustrated in FIG. 3. In theillustrated example of FIG. 3, the frame sampler 204 receives and/orotherwise retrieves an example video 302 from the video repository 202.The frame sampler 204 implements any appropriate sampling process toextract frames and/or images from the example video 302. In theillustrated example of FIG. 3, the extracted frames constitute exampleimages 304, including example image 306, example image 308 and exampleimage 310.

The number of example images 304 generated by the example frame sampler204 may be based on variables including, for example, the duration ofthe example video 302, the frequency at which the frame sampler 204samples and/or extracts the example video 302, and/or a threshold numberof frames that the frame sampler 204 is instructed to sample and/orextract from the example video 302. For example, if the video 302 has aduration of thirty seconds and the example frame sampler 204 extractsframes at a frequency and/or sampling rate of 30 frames per second, theexample frame sampler 204 will generate nine hundred (900) exampleimages 304 in the course of processing the example video 302. In anotherexample, the frame sampler 204 may be instructed to generate threehundred (300) example images 304 from the example video 302 regardlessof the duration of the video. In such an example, the frame sampler 204determines the duration of the example video 302, and then establishes asampling frequency and/or sampling rate that facilitates the generationof the specified number of example images 304.

In the illustrated examples of FIGS. 2 and 3, the example frame sampler204 may take the form of an executable video frame extractionapplication. For example, the frame sampler 204 may be implemented bythe downloadable application known as “FFmpeg” (available for downloadat https://www.ffmpeg.org/).

FIG. 4 is an illustration of the example image 306 generated by theexample video frame sampler 204 of FIGS. 2 and/or 3. In the illustratedexample of FIG. 4, the example image 306 includes an example object 402.In the illustrated example, the object 402 is a bottle of Coke® having alabel associated with The Coca-Cola Company, the label including theword “Alisha” among other textual information.

Returning to the illustrated example of FIG. 2, the example imagerepository 206 stores the example images 304 generated by the examplevideo frame sampler 204. The example image repository 206 of FIG. 2 isimplemented by one or more hard disk drives. However, the example imagerepository 206 could additionally or alternatively be implemented by anytype(s) and/or any number(s) of a storage drive, a storage disk, a flashmemory, a ROM, a CD, a DVD, a Blu-ray disc, a cache, a RAM and/or anyother storage medium in which information is stored for any duration(e.g., for extended time periods, permanently, brief instances, fortemporarily buffering, and/or for caching of the information). Theexample images 304 stored in the example image repository 206 may bestored in any file and/or data structure format, organization scheme,and/or arrangement. For example, an image (e.g., the example image 306of FIG. 3) may be stored as a Bitmap (.BMP), Better Portable Graphics(.BPG), Computer Graphics Metafile (.CGM), Graphics Interchange Format(.GIF), Joint Photographic Experts Group (.JPEG or .JPG), PortableNetwork Graphics (.PNG), Scalable Vector Graphics (.SVG), and/or TaggedImage File Format (.TIF and/or .TIFF) file.

The example images 304 stored in the example image repository 206 areaccessible to the example reverse image search engine 130 of FIGS. 1and/or 2, and/or, more generally, to the example video objectidentification system 100 of FIGS. 1 and/or 2. In some examples, theexample image repository 206 includes a webserver to which the exampleimages 304 are uploaded. In some such examples, the webserver isassociated with one or more Uniform Resource Locators (URLs) that may beused by the example reverse image search engine 130 of FIGS. 1 and/or 2to access the example images 304 residing on the webserver.

In the illustrated example of FIGS. 1 and/or 2, the example reverseimage search engine 130 searches for data that is relevant to and/orassociated with an image. In some examples, the reverse image searchengine 130 receives and/or otherwise retrieves the image from theexample image repository 206 described above. For example, the image 306described above in connection with FIGS. 3 and 4 may be uploaded to theexample reverse image search engine 130 by the example video objectidentification system 100. Additionally or alternatively, the examplevideo object identification system 100 may provide the example reverseimage search engine 130 with a URL corresponding to a webserver on whichthe example image 306 resides. In such an example, the reverse imagesearch engine 130 requests and/or accesses the example image 306 fromthe webserver based on the URL provided by the example video objectidentification system 100.

The example reverse image search engine 130 of FIGS. 1 and/or 2 isfurther illustrated in FIG. 5. In the illustrated example of FIG. 5, thereverse image search engine 130 receives and/or otherwise retrieves theexample image 306 as described above. In the illustrated example, thereverse image search engine 130 formulates a search query based on theexample image 306, as opposed to a search query based on textual searchterms (e.g., keywords and/or other terms) provided and/or selected by auser. In some examples, the example reverse image search engine 130formulates the search query by constructing a mathematical model of theexample image 306 using computer vision algorithms that may include, forexample, Scale-Invariant Feature Transform (SIFT), Maximally StableExtremal Regions (MSER) and/or Bag-of-Words (BoW) algorithms.

In the illustrated example of FIGS. 1, 2 and/or 5, the example reverseimage search engine 130 searches one or more data sources (e.g., anindex and/or a database) for data (e.g., images and/or metadataincluding textual and/or graphical information) that the reverse imagesearch engine 130 determines to match, be relevant to, and/or otherwisebe associated with the example image 306 based on the formulated searchquery. For example, if the example image 306 received and/or otherwiseretrieved by the example reverse image search engine 130 includes theexample object 402 described above in connection with FIG. 4, thereverse image search engine 130 may determine that textual and/orgraphical information relating to The Coca-Cola Company, Coke® products,and/or an advertising campaign (e.g., a commercial) affiliated with TheCoca-Cola Company and/or Coke® products is relevant to and/or associatedwith the example image 306. In some examples, the data identified by theexample reverse image search engine 130 may identify the source of theexample image 306 and/or one or more webpage(s) where the example image306 appears.

In the illustrated example of FIG. 5, the reverse image search engine130 generates an example search results page 502 that includes someand/or all of the data that the example reverse image search engine 130determines to be relevant to and/or associated with the example image306. The data included in the example search results page 502 may beorganized and/or otherwise structured according to any format determinedby the example reverse image search engine 130. For example, the reverseimage search engine 130 may organize the data included in the examplesearch results page 502 based on the relevance of the data. In such anexample, the data that the example reverse image search engine 130determines to be most relevant to the example image 306 may be organizedsuch that the most relevant data is positioned at the top of the examplesearch results page 502 when the search results page 502 is rendered fordisplay (e.g., when a browser application renders the search resultspage 502 for display on a display device, such as a computer monitor).

In the illustrated examples of FIGS. 1, 2 and 5, the example reverseimage search engine 130 may be hosted by a third party webserver. Forexample, the reverse image search engine 130 may be a reverse imagesearch engine hosted by Google, Inc. (e.g., Google® Images), MicrosoftCorp. (e.g., Bing® Image Match), Idee, Inc. (e.g., TinEye), etc.

Returning to the illustrated example of FIG. 2, the example searchresults page repository 208 stores search results pages (e.g., theexample search results page 502 of FIG. 5) generated by the examplereverse image search engine 130. The example search results pagerepository 208 of FIG. 2 is implemented by one or more hard disk drives.However, the example search results page repository 208 couldadditionally or alternatively be implemented by any type(s) and/or anynumber(s) of a storage drive, a storage disk, a flash memory, a ROM, aCD, a DVD, a Blu-ray disc, a cache, a RAM and/or any other storagemedium in which information is stored for any duration (e.g., forextended time periods, permanently, brief instances, for temporarilybuffering, and/or for caching of the information). The example searchresults page 502 stored in the example search results page repository208 may be stored in any file and/or data structure format, organizationscheme, and/or arrangement. For example, the search results page 502 maybe stored as a HyperText Markup (.HTM) and/or HyperText Markup Language(.HTML) file. The example search results page 502 stored in the examplesearch results page repository 208 is accessible to the example screencapturer 210 of FIG. 2, and/or, more generally, to the example videoobject identification system 100 of FIG. 1.

In the illustrated example of FIG. 2, the example screen capturer 210captures a screen shot of the search results page generated by theexample reverse image search engine 130. In some examples, the screencapturer 210 receives and/or otherwise retrieves the search results pagefrom the example search results page repository 208 described above. Forexample, the search results page 502 described above in connection withFIG. 5 may be accessed by the example screen capturer 210 from theexample search results page repository 208. Alternatively, the examplescreen capturer 210 may access the example search results page 502directly from the example reverse image search engine 130. In such anexample, the search results page repository 208 described above may beomitted from the video object identification system 100.

The example screen capturer 210 of FIG. 2 is further illustrated in FIG.6. In the illustrated example of FIG. 6, the screen capturer 210receives and/or otherwise retrieves the example search results page 502as described above. The example screen capturer 210 generates an examplescreen shot 602 corresponding to and/or associated with the examplesearch results page 502. In some examples, prior to the screen capturer210 generating the example screen shot 602, the example search resultspage 502 is rendered by a browser application for display on a displaydevice (e.g., a computer monitor). In some examples, the rendering ofthe example search results page is performed by the example screencapturer 210, and/or, more generally, by the example video objectidentification system 100.

In the illustrated examples of FIGS. 2 and 6, the example screencapturer 210 may be implemented by an executable screen captureapplication. For example, the screen capturer 210 may be implemented bythe downloadable application known as “webkit2png” (available fordownload at http://www.paulhammond.org/webkit2png/).

FIG. 7 is an illustration of the example screen shot 602 generated bythe example screen capturer 210 of FIGS. 2 and 6. In the illustratedexample of FIG. 7, the example screen shot 602 corresponds to theexample search results page 502 generated by the example reverse imagesearch engine 130 based on the example image 306 described above. In theillustrated example, the screen shot 602 depicts the example searchresults page 502 as rendered for display by a browser application. Asdescribed in detail herein, the example screen shot 602 is an image fromwhich textual information (e.g., the words “coke personalized bottle”depicted within the example screen shot 602 of FIG. 7) may be extractedand/or otherwise identified.

Returning to the illustrated example of FIG. 2, the example screen shotrepository 212 stores screen shots (e.g., the example screen shot 602 ofFIGS. 6 and 7) generated by the example screen capturer 210. The examplescreen shot repository 212 of FIG. 2 is implemented by one or more harddisk drives. However, the example screen shot repository 212 couldadditionally or alternatively be implemented by any type(s) and/or anynumber(s) of a storage drive, a storage disk, a flash memory, a ROM, aCD, a DVD, a Blu-ray disc, a cache, a RAM and/or any other storagemedium in which information is stored for any duration (e.g., forextended time periods, permanently, brief instances, for temporarilybuffering, and/or for caching of the information). The example screenshot 602 stored in the example screen shot repository 212 may be storedin any file and/or data structure format, organization scheme, and/orarrangement. For example, the screen shot 602 may be stored as a .BMP,.BPG, .CGM, .GIF, .JPEG, .JPG, .PNG, .SVG, .TIF, and/or .TIFF file. Theexample screen shot 602 stored in the example screen shot repository 212is accessible to the example text identifier 214 of FIG. 2, and/or, moregenerally, to the example video object identification system 100 of FIG.1.

In the illustrated example of FIG. 2, the example text identifier 214extracts and/or otherwise identifies textual information from a screenshot generated by the example screen capturer 210. In some examples, thetext identifier 214 receives and/or otherwise retrieves the screen shotfrom the example screen shot repository 212 described above. Forexample, the screen shot 602 described above in connection with FIGS. 6and 7 may be accessed by the example text identifier 214 from theexample screen shot repository 212. The example text identifier 214generates an example identified text data structure that includes theidentified textual information. The example text identifier 214 of FIG.2 is further illustrated in FIG. 8. In the illustrated example of FIG.8, the text identifier 214 receives and/or otherwise retrieves theexample screen shot 602 as described above. The example text identifier214 extracts and/or otherwise identifies textual information from theexample screen shot 602. In the illustrated example, the text identifier214 generates an example identified text data structure 802 thatincludes the identified textual information.

In the illustrated examples of FIGS. 2 and 8, the example textidentifier 214 may be implemented by an executable text identificationapplication and/or an executable optical character recognition (OCR)application. For example, the text identifier 214 may be implemented bythe downloadable OCR application known as “Tesseract” (available fordownload at http://code.google.com/p/tesseract-ocr/).

FIG. 9 is an illustration of the example identified text data structure802 generated by the example text identifier 214 of FIGS. 2 and/or 8. Inthe illustrated example of FIG. 9, the example identified text datastructure 802 corresponds to the example screen shot 602 generated bythe example screen capturer 210. For example, the identified text datastructure 802 includes example identified textual information 902 thatreplicates and/or closely approximates the textual information depictedin the example screen shot 602 of FIG. 7. As one example of theaforementioned relationship, the words “Best guess for this image: cokepersonalized bottle” appear among the textual information depicted inthe example screen shot 602, and also appear among the exampleidentified textual information 902 contained in the example identifiedtext data structure 802. In some examples, some or all textualinformation depicted in the example screen shot 602 will likewise appearamong the example identified textual information 902 contained in theexample identified text data structure 802. As a result of theaforementioned relationship between the example screen shot 602 and theexample data structure 802, the example identified textual information902 included in the example identified text data structure 802corresponds to and/or is associated with the example search results page502 generated by the example reverse image search engine 130 for theexample image 306 described above.

As a result of the sequence of screen capture and text identificationprocesses described above, the example video object identificationsystem 100 of FIGS. 1 and/or 2 advantageously implements an objectdetection process, system and/or apparatus that is parsing-presentationindependent. In other words, because the text identification process(e.g., as performed by the example text identifier 214) described aboveis based on the example screen shot 602, as opposed to being based onthe example search results page 502, the example video objectidentification system 100 need not parse out dynamic data that canchange at any time (e.g., HTML, XML, and/or JSON data) from the exampleidentified textual information 902, as otherwise might be necessary werethe text identification process instead based on the example searchresults page 502.

For example, in the case of an example search results page 502 thatincludes an HTML file, a text identification process applied directly tothe HTML file may involve parsing the HTML file to identify which piecesof the file are HTML tags and which pieces of the file are normal textof interest, etc. Once the HTML file has been parsed and its pieces havebeen identified, such a text identification process might then removethe pieces of the HTML file that do not constitute normal text ofinterest. In contrast, a screen shot (e.g., the example screen shot 602)of a search results page (e.g., the example search results page 502)will include the normal text of interest, but not any extraneous (e.g.,hidden) information such as HTML tags. Thus, the text identificationprocess disclosed herein, which is based on the example screen shot 602,as opposed to being based on the example search results page 502,removes the need for the example video object identification system 100to perform any parsing processes in relation to the example searchresults page 502.

Returning to the illustrated example of FIG. 2, the example identifiedtext repository 216 stores identified textual information (e.g., theexample identified textual information 902 contained in the exampleidentified text data structure 802 of FIGS. 8 and/or 9) generated by theexample text identifier 214. The example identified text repository 216of FIG. 2 is implemented by one or more hard disk drives. However, theexample identified text repository 216 could additionally oralternatively be implemented by any type(s) and/or any number(s) of astorage drive, a storage disk, a flash memory, a ROM, a CD, a DVD, aBlu-ray disc, a cache, a RAM and/or any other storage medium in whichinformation is stored for any duration (e.g., for extended time periods,permanently, brief instances, for temporarily buffering, and/or forcaching of the information). The example identified text data structure802 stored in the example identified text repository 216 may be storedin any file and/or data structure format, organization scheme, and/orarrangement. For example, the identified text data structure 802 may bestored as a text file (e.g., a .TXT file), a Microsoft® Word document(e.g., a .DOC file, a .DOCX file, etc.), etc. The example identifiedtext data structure 802 stored in the example identified text repository216 is accessible to the example base query record generator 218 of FIG.2, and/or, more generally, to the example video object identificationsystem 100 of FIG. 1.

In some examples, the video object identification system 100 of FIGS. 1and/or 2 repeats the processes described above in connection with theexample reverse image search engine 130, the example screen capturer 210and the example text identifier 214 for each of the example images 304generated by the example video frame sampler 204 in connection with theexample video 302. In some examples, the video object identificationsystem 100 repeats the processes described above in connection with theexample reverse image search engine 130, the example screen capturer 210and the example text identifier 214 for a subset of the set of theexample images 304 generated by the example video frame sampler 204 inconnection with the example video 302. For example, such a subset ofexample images 304 may be obtained by uniformly or non-uniformlysampling or otherwise selecting respective ones of the set of exampleimages 304 for inclusion in the subset.

In the illustrated example of FIG. 2, the example base query recordgenerator 218 generates base query records based on a query requestingthe identified textual information contained in the identified text datastructures generated by the example text identifier 214 that satisfiesone or more query criteria. In some examples, the base query recordgenerator 218 receives and/or otherwise retrieves the identified textdata structures on which to perform a query from the example identifiedtext repository 216 described above. For example, the identified textdata structure 802 described above in connection with FIGS. 8 and 9 maybe accessed by the example base query record generator 218 from theexample identified text repository 216.

The example base query record generator 218 of FIG. 2 is furtherillustrated in FIG. 10. In the illustrated example of FIG. 10, the basequery record generator 218 receives and/or otherwise retrieves exampleidentified text data structures 1002, which include the example datastructure 802 and example identified text data structures 1004 and 1006.In the illustrated example, the query is executed by the example basequery record generator 218 to generate example base query records 1010based on one or more query criteria including an example base searchterm 1008. In the illustrated example, the example base query records1010 include an example base query record 1012, an example base queryrecord 1014 and an example base query record 1016.

In some examples, the base search term 1008 is associated with a word, aphrase and/or a configuration or layout that is expected to be includedin the search results page returned by the example reverse image searchengine 130 to identify the data relevant to the input image that wassearched. For example, the reverse image search engine 130 may includethe phrase “Best guess for this image” as part of the example searchresults page 502 to identify the resulting data found to be mostrelevant to (e.g., identifying objects in) the input image (e.g., theexample image 306). As described above, this same phrase appears in theexample screen shot 602 and the example identified textual information902 of the example identified text data structure 802. In such anexample, the phrase “Best guess” may constitute the example base searchterm 1008 for the query executed by the example base query recordgenerator 218. Of course, other search terms and/or query criteria mayadditionally or alternatively be included in the query. For example, thephrase “Best Match” may constitute the example base search term 1008.Additionally or alternatively, the example base search term 1008 mayinvolve a position and/or threshold relative to the configuration orlayout of the example search results page 502 and/or the example screenshot 602. For example, the example base search term 1008 may specifythat the first ten lines of text, or the first one hundred words oftext, etc., are to be extracted from the identified text data structures1002 and included in the corresponding base query records 1010.

In the illustrated example of FIG. 10, the example base query recordgenerator 218 queries the example identified text data structures 1002to determine if any of the identified text data structures 1002 containidentified textual information matching the example base search term1008. In instances where the example base query record generator 218determines that one of the example identified text data structures 1002(e.g., identified text data structure 802) includes textual informationmatching the example base search term 1008, the example base queryrecord generator 218 generates a base query record (e.g., the examplebase query record 1012) corresponding to the identified text datastructure (e.g., the example identified text data structure 802). Insome examples, the generated example base query record 1012 includes theexample base search term 1008 as well as identified textual informationfrom the example identified text data structure 802 associated with(e.g., immediately following, on the same line as, and/or part of thesame string as) the example base search term 1008. As described below inconnection with the example object query record generator 222 of FIG.12, the inclusion of such associated identified textual information inthe generated base query record facilitates the identification of othersearch terms, such as, for example, the example object search term 1202of FIG. 12. In instances where the example base query record generator218 determines that one of the example identified text data structures1002 does not include textual information matching the example basesearch term 1008, the example base query record generator 218 does notgenerate a base query record corresponding to that identified text datastructure.

FIG. 11 is an illustration of an example base query record datastructure 1100 containing the example base query records 1010 generatedby the example base query record generator 218 of FIGS. 2 and/or 10based on the example base search term 1008. In the illustrated exampleof FIG. 11, the example base query records 1010 are organized asseparate lines within the example base query record data structure 1100.For example, the first line of the example base query record datastructure 1100 corresponds to the example base query record 1012described above, which in turn is associated with the example identifiedtext data structure 802, the example screen shot 602, the example searchresults page 502, and the example image 306. In the illustrated exampleof FIG. 11, the second line of the example base query record datastructure 1100 corresponds to the example base query record 1014described above. In the illustrated example of FIG. 11, the example basequery records 1010 can be segmented into two base query record groups. Afirst base query record group 1102 includes example base query recordshaving associated identified textual information including the phrase“coke personalized bottle” following the base search term 1008 (e.g.,“Best guess”). A second base query record group 1104 includes examplebase query records having associated identified textual informationincluding the phrase “share a coke with names list” following the basesearch term 1008 (e.g., “Best guess”).

While the example base query record data structure 1100 illustrated inFIG. 11 shows a specific number of example base query records 1010 and aspecific number of example base query record groups 1102, 1104, theexample base query record generator 218 may generate a base query recorddata structure 1100 having any number of example base query recordsand/or any number of example base query record groups. A base queryrecord group may include example base query records having similaridentified textual information regardless of whether the base queryrecords in the group are organized in consecutive and/or sequentiallines of the example base query record data structure 1100. Furthermore,a base query record group may include any number of base query records,including a single base query record.

Returning to the illustrated example of FIG. 2, the example base queryrecord repository 220 stores base query records (e.g., the example basequery records 1010 of FIG. 10 and/or the example base query record datastructure 1100 of FIG. 11) generated by the example base query recordgenerator 218. The example base query record repository 220 of FIG. 2 isimplemented by one or more hard disk drives. However, the example basequery record repository 220 could additionally or alternatively beimplemented by any type(s) and/or any number(s) of a storage drive, astorage disk, a flash memory, a ROM, a CD, a DVD, a Blu-ray disc, acache, a RAM and/or any other storage medium in which information isstored for any duration (e.g., for extended time periods, permanently,brief instances, for temporarily buffering, and/or for caching of theinformation). The example base query records 1010 stored in the examplebase query record repository 220 may be stored in any file and/or datastructure format, organization scheme, and/or arrangement. For example,the base query records 1010 may be stored as lines in the example basequery record data structure 1100 as shown in FIG. 11, with the examplebase query record data structure 1100 being stored in the example basequery record repository 220 as a text file (e.g., a .TXT file), aMicrosoft® Word document (e.g., a .DOC file, a .DOCX file, etc.), etc.The example base query records 1010 and/or the example base query recorddata structure 1100 stored in the example base query record repository220 are accessible to the example object query record generator 222and/or the example object identifier 226 of FIG. 2, and/or, moregenerally, to the example video object identification system 100 of FIG.1.

In the illustrated example of FIG. 2, the example object query recordgenerator 222 generates object query records based on a query of thebase query records (e.g., the example base query records 1010) generatedby the example base query record generator 218 that satisfies one ormore query criteria. In some examples, the object query record generator222 receives and/or otherwise retrieves the base query records on whichto perform a query from the example base query record repository 220described above. For example, the example base query records 1010described above in connection with FIGS. 10 and 11 and/or the examplebase query record data structure 1100 described above in connection withFIG. 11 may be accessed by the example object query record generator 222from the example base query record repository 220.

The example object query record generator 222 of FIG. 2 is furtherillustrated in FIG. 12. In the illustrated example of FIG. 12, theobject query record generator 222 receives and/or otherwise retrievesthe example base query records 1010, which include the example basequery record 1012, the example base query record 1014 and the examplebase query record 1016. In the illustrated example, the query isexecuted by the example object query record generator 222 to generateexample object query records 1204 based on one or more query criteriaincluding an example object search term 1202. In the illustratedexample, the example object query records 1204 include an example objectquery record 1206, an example object query record 1208 and an exampleobject query record 1210. In some examples, the object search term 1202is associated with a word and/or phrase that appears in at least one ofthe example base query records 1010. For example, based on the examplebase query record data structure 1100 illustrated in FIG. 11 anddescribed above, the example object search term 1202 applied by theexample object identifier 226 may be the phrase “coke personalizedbottle.” Alternatively, the example search term applied by the exampleobject identifier 226 may simply be the word “coke.”

In some examples, the specific word(s) and/or phrase(s) to be includedin the example object search term 1202 will be determined by the exampleobject query record generator 222 based on the frequency with which suchword(s) and/or phrase(s) appear among the textual information includedin the example base query records 1010 and/or in the example base queryrecord data structure 1100. For example, the object query recordgenerator 222 may determine via a string comparison analysis, a phrasecount analysis and/or a word count analysis that a particular wordand/or phrase is included among the example base query records 1010 witha heightened frequency relative to other words and/or phrases includedamong the example base query records 1010. In the course of performingsuch an analysis, the example object query record generator 222 mayignore and/or filter out the example base search term 1008, as theexample base search term 1008 appears in each of the example base queryrecords 1010. For example, the example object query record generator 222may determine that the phrase “coke personalized bottle” appears morefrequently in the example base query record data structure 1100 of FIG.11 relative to other phrases appearing in the example base query recorddata structure, such as the phrase “share a coke with names list.” Inmaking this determination, the example object record generator 222ignores the phrase “Best guess,” as such phrase constitutes the examplebase search term 1008 which appears in each of the example base queryrecords 1010. In such an example, the object query record generator 222determines that the phrase “coke personalized bottle” will be theexample object search term 1202.

In the illustrated example of FIG. 12, the example object query recordgenerator 222 queries the example base query records 1010 to determineif any of the base query records 1010 contain textual informationmatching the example object search term 1202. In instances where theexample object query record generator 222 determines that one of theexample base query records 1010 (e.g., the example base query record1012) includes textual information matching the example object searchterm 1202, the example object query record generator 222 generates anobject query record (e.g., the example object query record 1206)corresponding to the base query record (e.g., the example base queryrecord 1012). In some examples, the generated example object queryrecord 1206 includes the example object search term 1202 as well as theexample base search term 1008 from which the corresponding example basequery record 1012 was generated. In instances where the example objectquery record generator 222 determines that one of the example base queryrecords 1010 does not include textual information matching the exampleobject search term 1202, the example object query record generator 222does not generate an object query record corresponding to that basequery record.

FIG. 13 is an illustration of an example object query record datastructure 1300 containing the example object query records 1204generated by the example object query record generator 222 of FIGS. 2and/or 12 based on the example object search term 1202. In theillustrated example of FIG. 13, the example object query records 1204are organized as separate lines within the example object query recorddata structure 1300. For example, the first line of the example objectquery record data structure 1300 corresponds to the example object queryrecord 1206 described above, which in turn is associated with theexample base query record 1012, the example identified text datastructure 802, the example screen shot 602, the example search resultspage 502, and the example image 306. In the illustrated example of FIG.13, the second line of the example object query record data structure1300 corresponds to the example object query record 1208 describedabove. In the illustrated example of FIG. 13, the example object queryrecords 1204 include textual information corresponding to the exampleobject search term 1202 (e.g., “coke personalized bottle”) as well astextual information corresponding to the example base search term 1008(e.g., “Best guess”) from which the corresponding base query recordswere generated. While the example object query record data structure1300 illustrated in FIG. 13 shows a specific number of example objectquery records 1204, the example object query record generator 222 maygenerate an object query record data structure 1300 having any number ofexample object base query records.

Returning to the illustrated example of FIG. 2, the example object queryrecord repository 224 stores object query records (e.g., the exampleobject query records 1204 of FIG. 12 and/or the example object queryrecord data structure 1300 of FIG. 13) generated by the example objectquery record generator 222. The example object query record repository224 of FIG. 2 is implemented by one or more hard disk drives. However,the example object query record repository 224 could additionally oralternatively be implemented by any type(s) and/or any number(s) of astorage drive, a storage disk, a flash memory, a ROM, a CD, a DVD, aBlu-ray disc, a cache, a RAM and/or any other storage medium in whichinformation is stored for any duration (e.g., for extended time periods,permanently, brief instances, for temporarily buffering, and/or forcaching of the information). The example object query records 1204stored in the example object query record repository 224 may be storedin any file and/or data structure format, organization scheme, and/orarrangement. For example, the object query records 1204 may be stored aslines in the example object query record data structure 1300 as shown inFIG. 13, with the example object query record data structure 1300 beingstored in the example object query record repository 224 as a text file(e.g., a .TXT file), a Microsoft® Word document (e.g., a .DOC file, a.DOCX file, etc.), etc. As described in detail herein, the exampleobject query records 1204 and/or the example object query record datastructure 1300 stored in the example object query record repository 224are accessible to the example object identifier 226 of FIG. 2, and/or,more generally, to the example video object identification system 100 ofFIG. 1.

In the illustrated example of FIG. 2, the example object identifier 226identifies one or more objects (e.g., the example object 402 discussedabove in connection with FIG. 4) depicted in a video based on astatistical criterion applied to the object query records generated bythe example object query record generator 222 and/or the base queryrecords generated by the example base query record generator 218 for thevideo. In some examples, the object identifier 226 receives and/orotherwise retrieves the object query records from the example objectquery record repository 224 described above. For example, the objectquery records 1204 and/or the object query record data structure 1300described above in connection with FIGS. 12 and 13 may be accessed bythe example object identifier 226 from the example object query recordrepository 224. In some examples, the object identifier 226 alsoreceives and/or otherwise retrieves the base query records from theexample base query record repository 220 described above. For example,the base query records 1010 and/or the base query record data structure1100 described above in connection with FIGS. 10 and 11 may be accessedby the example object identifier 226 from the example base query recordrepository 220.

The example object identifier 226 of FIG. 2 is further illustrated inFIG. 14. In the illustrated example of FIG. 14, the object identifier226 receives and/or otherwise retrieves the example object query records1204 associated with a given video or a given portion of a video. In theillustrated example, the example object query records 1204 include theexample object query record 1206, the example object query record 1208and the example object query record 1210. The example object identifier226 also receives and/or otherwise retrieves the example base queryrecords 1010 associated with a given video or a given portion of avideo. In the illustrated example, the example base query records 1010include the example base query record 1012, the example base queryrecord 1014 and the example base query record 1016.

In the illustrated example of FIG. 14, the example object identifierapplies an example statistical criterion 1402 to the example objectquery records 1204 and/or the example base query records 1010. In someexamples, the statistical criterion 1402 is a threshold specifying atotal number of object query records to be generated by the exampleobject query record generator 222 for the example object identifier 226to identify an object. For example, the statistical criterion 1402 mayspecify that the example object identifier 226 identifies an object if atotal of at least ten object query records (or some other number ofobject query records) have been generated by the example object queryrecord generator 222 in relation to the example object search term 1202.The example object identifier 226 determines whether such an examplestatistical criterion 1402 is satisfied by determining (e.g., counting)the number of object query records (e.g., the number of lines in theexample object query record data structure 1300 of FIG. 13) generated bythe example object query record generator 222 in relation to the exampleobject search term 1202. When applying the example statistical criterion1402 described above to the example object query record data structure1300 of FIG. 13, the example object identifier 226 determines that thestatistical criterion 1402 is satisfied because the number of objectquery records 1204 (e.g., fifteen) satisfies the threshold numberspecified by the statistical criterion 1402 (e.g., ten).

In some examples, the statistical criterion 1402 is a thresholdspecifying a number of the object query records within a specified timeperiod to be generated by the example object query record generator 222for the example object identifier 226 to identify an object. In somesuch examples, the specified time period is based on the sampling rateat which the images corresponding to the object query records wereextracted from the video. For example, the statistical criterion 1402may specify that the example object identifier 226 identifies an objectif at least ten object query records (or some other number of objectquery records) have been generated by the example object query recordgenerator 222 in relation to the example object search term 1202, andmay further specify that the qualifying object query records must spanno more than ten consecutive seconds of time associated with the videofrom which the object query records have been derived. The exampleobject identifier 226 determines whether the example statisticalcriterion 1402 is satisfied by determining (e.g., counting) the numberof object query records (e.g., the number of lines in the example objectquery record data structure 1300 of FIG. 13) generated by the exampleobject query record generator 222 in relation to the example objectsearch term 1202, and then determining whether a sufficient number ofthose object query records fall within the specified time period. Whenapplying the example statistical criterion 1402 described above to theexample object query record data structure 1300 of FIG. 13, the exampleobject identifier 226 determines that the statistical criterion 1402 issatisfied because, first, the number of object query records 1204 (e.g.,fifteen) satisfies the threshold number specified by the statisticalcriterion 1402 (e.g., ten) and, second, the example object query records1204 span a total of 0.5 seconds of time associated with the video fromwhich those object query records have been derived, which falls withinthe specified time period (e.g., ten consecutive seconds). In the aboveexample, the time associated with the video from which the object queryrecords 1204 have been derived is computed by dividing the fifteenconsecutively numbered object query records 1204 (corresponding to thefirst fifteen frames and/or example images 304 extracted from theexample video 302) by the sampling rate (e.g., thirty frames per second)that was utilized when extracting the example images 304 from which theexample object query records 1204 have been derived.

In some examples, the statistical criterion 1402 is a thresholdspecifying a consecutive number of object query records to be generatedby the example object query record generator 222 for the example objectidentifier 226 to identify an object. For example, the statisticalcriterion 1402 may specify that the example object identifier 226identifies an object if at least ten consecutively numbered object queryrecords (or some other number of consecutive object query records) havebeen generated by the example object query record generator 222 inrelation to the example object search term 1202. For example, theexample object query record data structure 1300 of FIG. 13 includesfifteen consecutively numbered object query records 1204 identified as“result1.txt,” “result2.txt,” “result3.txt” and continuing consecutivelythrough “result15.txt.” The example object identifier 226 determineswhether the example statistical criterion 1402 is satisfied bydetermining (e.g., counting) the consecutively numbered object queryrecords (e.g., the consecutively numbered lines in the example objectquery record data structure 1300 of FIG. 13) generated by the exampleobject query record generator 222 in relation to the example objectsearch term 1202. When applying the example statistical criterion 1402described above to the example object query record data structure 1300of FIG. 13, the example object identifier 226 determines that thestatistical criterion 1402 is satisfied because the number ofconsecutive object query records 1204 (e.g., fifteen) satisfies thethreshold number specified by the statistical criterion 1402 (e.g.,ten).

In some examples, the statistical criterion 1402 is a thresholdspecifying a percentage of the number of object query records generatedby the example object query record generator 222 for a particular objectsearch term 1202 in relation to the number of base query recordsgenerated by the example base query record generator 218 to be satisfiedfor the example object identifier 226 to identify an object in the inputvideo as corresponding to the particular object search term 1202. Forexample, the statistical criterion 1402 may specify that the number ofobject query records generated by the example object query recordgenerator 222 for a particular object search term 1202 constitute atleast one percent (or some other percentage) of the number of base queryrecords generated by the example base query record generator 218 inorder for the object identifier 226 to determine that an objectcorresponding to the particular object search term 1202 is depicted inthe input video being processed. The example object identifier 226determines whether such an example statistical criterion 1402 issatisfied by determining (e.g., counting) the number of object queryrecords generated by the example object query record generator 222 for aparticular object search term 1202 (e.g., the number of lines in theexample object query record data structure 1300 of FIG. 13), as well asthe number of base query records for the input video (e.g., the numberof lines in the example base query record data structure 1100 of FIG.11) generated by the example base query record generator 218. Theexample object identifier 226 then calculates a percentage by dividingthe determined number of object query records by the determined numberof base query records, and multiplying the result by a factor of onehundred. When applying the example statistical criterion 1402 describedabove to the example base query record data structure 1100 of FIG. 11and the example object query record data structure 1300 of FIG. 13, theexample object identifier 226 may determine that a total of fifteenobject query records exist for the object search term 1202 given by“coke personalized bottle,” and a total of nine hundred base queryrecords exist. Thus, the percentage calculated by the example objectidentifier 226 will be equal to 1.67 percent (e.g., fifteen object queryrecords divided by nine hundred base query records, multiplied by afactor of one hundred). In such an example, the object identifier 226determines that the example statistical criterion 1402 is satisfiedbecause the percentage of object query records 1204 in relation to basequery records 1010 (e.g., 1.67 percent) satisfies the thresholdpercentage specified by the statistical criterion (e.g., 1.00 percent).

In the illustrated examples of FIGS. 11-14 described above, when thestatistical criterion is satisfied, the example object identifier 226generates an example identified object record 1404 that identifies anobject depicted in the example video 302. In the illustrated examples ofFIGS. 11-14, the example identified object record 1404 may indicate thatthe example video 302 depicts an object constituting a “cokepersonalized bottle.” As described above in connection with FIGS. 3 and4, the example image 306 that was extracted from the example video 302included an example object 402 appearing as a bottle of Coke® having alabel associated with The Coca-Cola Company, the label including theword “Alisha” among other textual information. Thus, in the illustratedexample, the example object identifier 226, and/or, more generally, theexample video object identification system 100, has properly identifiedthe example object 402 (e.g., a bottle of Coke® labelled with the name“Alisha”) depicted in the example video 302 as a “coke personalizedbottle.”

While the examples described above result in the example video objectidentification system 100 identifying an object including a brand (e.g.,Coke®), the object identified by the example video object identificationsystem 100 may additionally and/or alternatively include other types ofinformation such as, for example, a logo (e.g., the “wave logo” of TheCoca-Cola Company) and/or a commercial or advertising campaign (e.g.,The Coca-Cola Company's “Share A Coke” campaign, or a commercialassociated therewith), etc.

FIG. 15 is an example script 1500 including example machine-readableinstructions that may be executed to implement the example video objectidentification system 100 of FIGS. 1 and/or 2. In the illustratedexample, the script 1500 includes an example first portion of code 1502.The first portion of code 1502 instructs the example video frame sampler204 of the example video object identification system 100 to extractand/or sample images (e.g., the example images 304) from a video (e.g.,the example video 302). In the illustrated example, the script 1500includes an example second portion of code 1504. The second portion ofcode 1504 instructs the example video object identification system 100to upload the extracted images (e.g., the example images 304) to awebserver.

In the illustrated example of FIG. 15, the script 1500 includes anexample third portion of code 1506. The third portion of code 1506instructs the example video object identification system 100 to begin animage processing cycle to be applied to each of the extracted anduploaded images (e.g., the example images 304). In the illustratedexample, the script 1500 includes an example fourth portion of code1508. The fourth portion of code 1508 instructs the example video objectidentification system 100, for each extracted image, to provide areverse image search engine (e.g., the example reverse image searchengine 130) with a URL corresponding to the webserver to which theextracted image (e.g., the example image 306) was uploaded. The fourthportion of code further instructs the example screen capturer 210 of theexample video object identification system 100, for each extractedimage, to capture a screen shot (e.g., the example screen shot 602)corresponding to a search results page (e.g., the example search resultspage 502) returned by the reverse image search engine. In theillustrated example, the script 1500 includes an example fifth portionof code 1510. The fifth portion of code 1510 instructs the example textidentifier 214 of the example video object identification system 100,for each extracted image, to identify textual information (e.g., theexample identified text data structure 802) from the screen shot (e.g.,the example screen shot 602) generated by the example screen capturer210. In the illustrated example, the script 1500 includes an examplesixth portion of code 1512. The sixth portion of code 1512 instructs theexample video object identification system 100 to complete the imageprocessing cycle once all of the images (e.g., the example images 304)have been processed.

In the illustrated example of FIG. 15, the script 1500 includes anexample seventh portion of code 1514. The seventh portion of code 1514instructs the example base query record generator 218 of the examplevideo object identification system 100 to execute a query of theidentified text data structures (e.g., the example identified text datastructures 1002) based on a base search term (e.g., the example basesearch term 1008) to determine base query records (e.g., the examplebase query records 1010) containing identified text data matching thebase search term. In the illustrated example of FIG. 15, the script 1500includes an example eighth portion of code 1516. The eighth portion ofcode 1516 instructs the example object query record generator 222 of theexample video object identification system 100 to execute a query of thebase query records (e.g., the example base query records 1010) based onan object search term (e.g., the example object search term 1202) todetermine object query records (e.g., the example object query records1204) containing identified text data matching the object search term.

In the illustrated example, the script 1500 includes an example ninthportion of code 1518. The ninth portion of code 1518 instructs theexample object identifier 226 of the example video object identificationsystem 100 to determine the number of base query records (e.g., theexample base query records 1010) generated by the example base queryrecord generator 218 for the base search term (e.g., the example basesearch term 1008) and the number of object query records (e.g., theexample object query records 1204) generated by the example object queryrecord generator 222 for the object search term (e.g., the exampleobject search term 1202) for the input video.

In the illustrated example of FIG. 15, the example script 1500, based onthe sequence of processes described above, provides the example videoobject identification system 100 with sufficient information to enablethe example object identifier 226 to determine whether a statisticalcriterion (e.g., the example statistical criterion 1402) is satisfied.In instances where the example object identifier 226 determines that thestatistical criterion is satisfied, the example object identifier 226generates an identified object record (e.g., the example identifiedobject record 1404) identifying an object (e.g., the example object 402)depicted in a video (e.g., the example video 302).

While an example manner of implementing the example video objectidentification system 100 is illustrated in FIGS. 1-3, 6, 8, 10, 12 and14, one or more of the elements, processes and/or devices illustrated inFIGS. 1-3, 6, 8, 10, 12 and 14 may be combined, divided, re-arranged,omitted, eliminated and/or implemented in any other way. For example,the example video repository 202, the example image repository 206, theexample search results page repository 208, the example screen shotrepository 212, the example identified text repository 216, the examplebase query record repository 220 and the example object query recordrepository 224 may be combined into a single repository that stores theexample videos, images, search results pages, screen shots, identifiedtext data structures, base query records and object query recordsdescribed above. Further, the example video repository 202, the examplevideo frame sampler 204, the example image repository 206, the examplesearch results page repository 208, the example screen capturer 210, theexample screen shot repository 212, the example text identifier 214, theexample identified text repository 216, the example base query recordgenerator 218, the example base query record repository 220, the exampleobject query record generator 222, the example object query recordrepository 224, the example object identifier 226, and/or, moregenerally, the example video object identification system 100 of FIGS.1-3, 6, 8, 10, 12 and/or 14 may be implemented by hardware, software,firmware and/or any combination of hardware, software and/or firmware.Thus, for example, any of the example video repository 202, the examplevideo frame sampler 204, the example image repository 206, the examplesearch results page repository 208, the example screen capturer 210, theexample screen shot repository 212, the example text identifier 214, theexample identified text repository 216, the example base query recordgenerator 218, the example base query record repository 220, the exampleobject query record generator 222, the example object query recordrepository 224, the example object identifier 226, and/or, moregenerally, the example video object identification system 100 of FIGS.1-3, 6, 8, 10, 12 and/or 14 could be implemented by one or more analogor digital circuit(s), logic circuits, programmable processor(s),application specific integrated circuit(s) (ASIC(s)), programmable logicdevice(s) (PLD(s)) and/or field programmable logic device(s) (FPLD(s)).When reading any of the apparatus or system claims of this patent tocover a purely software and/or firmware implementation, at least one ofthe example video repository 202, the example video frame sampler 204,the example image repository 206, the example search results pagerepository 208, the example screen capturer 210, the example screen shotrepository 212, the example text identifier 214, the example identifiedtext repository 216, the example base query record generator 218, theexample base query record repository 220, the example object queryrecord generator 222, the example object query record repository 224,and/or the example object identifier 226 of FIGS. 2-3, 6, 8, 10, 12and/or 14 is/are hereby expressly defined to include a tangible computerreadable storage device or storage disk such as a memory, a digitalversatile disk (DVD), a compact disk (CD), a Blu-ray disk, etc. storingthe software and/or firmware. Further still, the example video objectidentification system 100 of FIGS. 1-3, 6, 8, 10, 12 and 14 may includeone or more elements, processes and/or devices in addition to, orinstead of, those illustrated in FIGS. 1-3, 6, 8, 10, 12 and 14, and/ormay include more than one of any or all of the illustrated elements,processes and devices.

A flowchart representative of example machine readable instructions forimplementing the example video object identification system 100 of FIGS.1-3, 6, 8, 10, 12 and 14 is shown in FIG. 16. In this example, themachine readable instructions comprise a program for execution by aprocessor such as the processor 1712 shown in the example processorplatform 1700 discussed below in connection with FIG. 17. The programmay be embodied in software stored on a tangible computer readablestorage medium such as a CD-ROM, a floppy disk, a hard drive, a digitalversatile disk (DVD), a Blu-ray disk, or a memory associated with theprocessor 1712, but the entire program and/or parts thereof couldalternatively be executed by a device other than the processor 1712and/or embodied in firmware or dedicated hardware. Further, although theexample program is described with reference to the flowchart illustratedin FIG. 16, many other methods of implementing the example video objectidentification system 100 may alternatively be used. For example, theorder of execution of the blocks may be changed, and/or some of theblocks described may be changed, eliminated, or combined.

As mentioned above, the example processes of FIG. 16 may be implementedusing coded instructions (e.g., computer and/or machine readableinstructions) stored on a tangible computer readable storage medium suchas a hard disk drive, a flash memory, a read-only memory (ROM), acompact disk (CD), a digital versatile disk (DVD), a cache, arandom-access memory (RAM) and/or any other storage device or storagedisk in which information is stored for any duration (e.g., for extendedtime periods, permanently, for brief instances, for temporarilybuffering, and/or for caching of the information). As used herein, theterm tangible computer readable storage medium is expressly defined toinclude any type of computer readable storage device and/or storage diskand to exclude propagating signals and to exclude transmission media. Asused herein, “tangible computer readable storage medium” and “tangiblemachine readable storage medium” are used interchangeably. Additionallyor alternatively, the example processes of FIG. 16 may be implementedusing coded instructions (e.g., computer and/or machine readableinstructions) stored on a non-transitory computer and/or machinereadable medium such as a hard disk drive, a flash memory, a read-onlymemory, a compact disk, a digital versatile disk, a cache, arandom-access memory and/or any other storage device or storage disk inwhich information is stored for any duration (e.g., for extended timeperiods, permanently, for brief instances, for temporarily buffering,and/or for caching of the information). As used herein, the termnon-transitory computer readable medium is expressly defined to includeany type of computer readable storage device and/or storage disk and toexclude propagating signals and to exclude transmission media. As usedherein, when the phrase “at least” is used as the transition term in apreamble of a claim, it is open-ended in the same manner as the term“comprising” is open ended.

FIG. 16 is a flowchart representative of example machine-readableinstructions 1600 that may be executed to implement the example videoobject identification system 100 of FIGS. 1 and/or 2. The program 1600of FIG. 16 begins when the example video frame sampler 204 of FIGS. 2and 3 extracts and/or samples images (e.g., the example images 304) froma video (e.g., the example video 302) (block 1602).

Once the example video frame sampler 204 has extracted one or moreimages from the input video, the example program 1600 of FIG. 16 beginsan image processing cycle to be executed for each extracted image onwhich object identification is to be performed (block 1604). In theimage processing cycle, the example video object identification system100 of FIGS. 1 and/or 2 provides an image (e.g., the example image 306)to the example reverse image search engine 130 of FIGS. 1, 2 and/or 5which generates a search results page (e.g., the example search resultspage 502) associated with the image (e.g., the example image 306) (block1606). In the image processing cycle, the example screen capturer 210 ofFIGS. 2 and/or 6 also captures a screen shot (e.g., the example screenshot 602) of the search results page (e.g., the example search resultspage 502) (block 1608). In the image processing cycle, the example textidentifier 214 of FIGS. 2 and/or 8 further identifies textualinformation (e.g., the example identified text data structure 802) fromthe screen shot (e.g., the example screen shot 602) generated by theexample screen capturer 210 (block 1610).

The example program 1600 of FIG. 16 repeats the image processing cycledescribed above until the program 1600 and/or the example video objectidentification system 100 determines that processing of the extractedimages (e.g., the example images 304) is complete (block 1612). Oncesuch a determination has been made, the example base query recordgenerator 218 of FIGS. 2 and 10 executes a query of the identified textdata structures (e.g., the example identified text data structures 1002)based on a base search term (e.g., the example base search term 1008)(block 1614), and generates base query records (e.g., the example basequery records 1010) corresponding to the base query results (block1616).

Next, the example object query record generator 222 of FIGS. 2 and 12executes a query of the base query records (e.g., the example base queryrecords 1010) based on an object search term (e.g., the example objectsearch term 1202) (block 1618), and generates object query records(e.g., the example object query records 1204) corresponding to theobject query results (block 1620). The example object query recordgenerator 222 determines whether to execute another query of the basequery records (e.g., the example base query records 1010) based on adifferent object search term (block 1622). If the object query recordgenerator 222 determines to execute another query, control of theprogram 1600 returns to block 1618. If the object query record generator222 determines not to execute another query, the program 1600 proceedsto block 1624.

Next, the example object identifier 226 of FIGS. 2 and/or 14 identifiesone or more objects (e.g., the example object 402) depicted in the video(e.g., the example video 302) based on whether one or more statisticalcriteria (e.g., the example statistical criterion 1402) applied to theobject query records (e.g., the example object query records 1204)and/or the base query records (e.g., the example base query records1010) is/are satisfied (block 1624). The statistical criterion 1402 mayinclude, for example, a specified total number of object query recordsassociated with a particular object search term, a specified number ofobject query records associated with a particular object search termwithin a specified time period, a specified consecutive number of objectquery records associated with a particular object search term, and/or aspecified percentage of the number of object query records associatedwith a particular object search term relative to the number of basequery records from which the object query records were derived. Inconnection with identifying an object depicted in the video at block1624, the example object identifier 226 may generate an identifiedobject record (e.g., the example identified object record 1404) thatdescribes the identified object.

FIG. 17 is a block diagram of an example processor platform 1700structured to execute the example script of FIG. 15 and/or the exampleinstructions of FIG. 16 to implement the example video objectidentification system 100 of FIGS. 1-3, 6, 8, 10, 12 and 14. Theprocessor platform 1700 can be, for example, a server, a personalcomputer, a mobile device (e.g., a cell phone, a smart phone, a tabletsuch as an iPad™), a personal digital assistant (PDA), or any other typeof computing device.

The processor platform 1700 of the illustrated example includes aprocessor 1712. The processor 1712 of the illustrated example ishardware. For example, the processor 1712 can be implemented by one ormore integrated circuits, logic circuits, microprocessors or controllersfrom any desired family or manufacturer.

The processor 1712 of the illustrated example executes exampleinstructions 1500, 1600 to implement the example video frame sampler204, the example screen capturer 210, the example text identifier 214,the example base query record generator 218, the example object queryrecord generator 222 and the example object identifier 226 describedabove in connection with FIGS. 2-3, 6, 8, 10, 12 and 14. The exampleprocessor 1712 also includes a local memory 1714 (e.g., a cache). Theprocessor 1712 of the illustrated example is in communication with amain memory including a volatile memory 1716 and a non-volatile memory1718 via a bus 1720. The volatile memory 1716 may be implemented bySynchronous Dynamic Random Access Memory (SDRAM), Dynamic Random AccessMemory (DRAM), RAMBUS Dynamic Random Access Memory (RDRAM) and/or anyother type of random access memory device. The non-volatile memory 1718may be implemented by flash memory and/or any other desired type ofmemory device. Access to the main memory 1716, 1718 is controlled by amemory controller.

The processor platform 1700 of the illustrated example also includes aninterface circuit 1722. The interface circuit 1722 may be implemented byany type of interface standard, such as an Ethernet interface, auniversal serial bus (USB), and/or a PCI express interface.

In the illustrated example, one or more input devices 1724 are connectedto the interface circuit 1722. The input device(s) 1724 permit(s) a userto enter data and commands into the processor 1712. The input device(s)can be implemented by, for example, a keyboard, a mouse, a touchscreen,a track-pad, a trackball, isopoint and/or a voice recognition system.

One or more output devices 1726 are also connected to the interfacecircuit 1722 of the illustrated example. The output devices 1726 can beimplemented, for example, by display devices (e.g., a liquid crystaldisplay, a cathode ray tube display (CRT), a touchscreen, a printerand/or speakers). The interface circuit 1722 of the illustrated example,thus, typically includes a graphics driver card, a graphics driver chipor a graphics driver processor.

The interface circuit 1722 of the illustrated example also includes acommunication device such as a transmitter, a receiver, a transceiver, amodem and/or network interface card to facilitate exchange of data withexternal machines (e.g., computing devices of any kind) via a network1728 (e.g., an Ethernet connection, a digital subscriber line (DSL), atelephone line, coaxial cable, a cellular telephone system, etc.).

The processor platform 1700 of the illustrated example also includes oneor more mass storage devices 1730 for storing software and/or data.Examples of such mass storage devices 1730 include floppy disk drives,hard drive disks, compact disk drives, Blu-ray disk drives, RAIDsystems, and digital versatile disk (DVD) drives. In the illustratedexample, the mass storage device 1730 includes the example videorepository 202, the example image repository 206, the example searchresults page repository 208, the example screen shot repository 212, theexample identified text repository 216, the example base query recordrepository 220 and the example object query record repository 224.

The coded instructions 1732 of FIG. 17, which includes the exampleinstructions described above in connection with FIGS. 15 and/or 16, maybe stored in the mass storage device 1730, in the local memory 1714, inthe volatile memory 1716, in the non-volatile memory 1718, and/or on aremovable tangible computer readable storage medium such as a CD or DVD.

Although certain example methods, apparatus and articles of manufacturehave been disclosed herein, the scope of coverage of this patent is notlimited thereto. On the contrary, this patent covers all methods,apparatus and articles of manufacture fairly falling within the scope ofthe claims of this patent.

What is claimed is:
 1. A method to identify an object depicted in avideo, the method comprising: identifying textual information presentedin search results pages returned by a reverse image search engine forrespective ones of a plurality of images extracted from the video;generating a plurality of base query records corresponding to respectiveones of the search results pages that have respective textualinformation satisfying a base search term; generating a plurality ofobject query records corresponding to respective ones of the base queryrecords that satisfy an object search term; and applying, with aprocessor, a statistical criterion to the plurality of object queryrecords to identify the object depicted in the video.
 2. A method asdefined in claim 1, wherein the reverse image search engine is hosted bya webserver.
 3. A method as defined in claim 1, wherein identifying thetextual information includes capturing respective screen shots of thesearch results pages.
 4. A method as defined in claim 1, whereinidentifying the textual information includes executing an opticalcharacter recognition application.
 5. A method as defined in claim 1,wherein the statistical criterion includes a threshold based on a firstnumber of the object query records including the object search term. 6.A method as defined in claim 1, wherein the statistical criterionincludes a threshold based on a first number of the object query recordsincluding the object search term within a first time period, the firsttime period based on a sampling rate at which the images correspondingto the object query records were extracted from the video.
 7. A methodas defined in claim 1, wherein the statistical criterion includes athreshold based on a first consecutive number of the object queryrecords including the object search term.
 8. A method as defined inclaim 1, wherein the statistical criterion includes a threshold based ona first percentage of a number of object query records including theobject search term relative to a number of base query records includingthe base search term.
 9. A method as defined in claim 1, wherein theobject depicted in the video includes at least one of a logo, a brand,or a commercial.
 10. A system to identify an object depicted in a video,the system comprising: a text identifier to access, for respective onesof a plurality of images extracted from the video and provided to areverse image search engine, a search results page returned by thereverse image search engine for the image, and to identify, forrespective ones of the search results pages, textual informationpresented in the search results page; and an object identifier toidentify the object depicted in the video based on a statisticalcriterion applied to a plurality of object query records generated froma respective plurality of base query records determined to include anobject search term, the base query records corresponding to respectiveones of the images having respective search results pages determined tohave respective textual information including a base search term.
 11. Asystem as defined in claim 10, further including a video frame samplerto extract the images from the video.
 12. A system as defined in claim10, further including a screen capturer to capture a screen shot of thesearch results page.
 13. A system as defined in claim 10, wherein thetext identifier is to execute an optical character recognitionapplication.
 14. A system as defined in claim 10, wherein thestatistical criterion includes a threshold based on a first number ofthe object query records including the object search term.
 15. A systemas defined in claim 10, wherein the statistical criterion includes athreshold based on a first number of the object query records includingthe object search term within a first time period, the first time periodbased on a sampling rate at which the images corresponding to the objectquery records were extracted from the video.
 16. A system as defined inclaim 10, wherein the statistical criterion includes a threshold basedon a first consecutive number of the object query records including theobject search term.
 17. A system as defined in claim 10, wherein thestatistical criterion includes a threshold based on a first percentageof a number of object query records including the object search termrelative to a number of base query records including the base searchterm.
 18. A system as defined in claim 10, wherein the object depictedin the video includes at least one of a logo, a brand, or a commercial.19. A tangible computer readable storage medium comprising computerreadable instructions that, when executed, cause a processor to atleast: identify textual information presented in search results pagesreturned by a reverse image search engine for respective ones of aplurality of images extracted from a video; generate a plurality of basequery records corresponding to respective ones of the search resultspages that have respective textual information satisfying a base searchterm; generate a plurality of object query records corresponding torespective ones of the base query records that satisfy an object searchterm; and apply a statistical criterion to the plurality of object queryrecords to identify an object depicted in the video.
 20. A storagemedium as defined in claim 19, wherein the reverse image search engineis hosted by a webserver.
 21. A storage medium as defined in claim 19,wherein to identify the textual information, the instructions furthercause the processor to capture respective screen shots of the searchresults pages.
 22. A storage medium as defined in claim 19, wherein theinstructions further cause the processor to execute an optical characterrecognition application to identify the textual information presented inthe respective ones of the search results pages.
 23. A storage medium asdefined in claim 19, wherein the statistical criterion includes athreshold based on a first number of the object query records includingthe object search term.
 24. A storage medium as defined in claim 19,wherein the statistical criterion includes a threshold based on a firstnumber of the object query records including the object search termwithin a first time period, the first time period based on a samplingrate at which the images corresponding to the object query records wereextracted from the video.
 25. A storage medium as defined in claim 19,wherein the statistical criterion includes a threshold based on a firstconsecutive number of the object query records including the objectsearch term.
 26. A storage medium as defined in claim 19, wherein thestatistical criterion includes a threshold based on a first percentageof a number of object query records including the object search termrelative to a number of base query records including the base searchterm.
 27. A storage medium as defined in claim 19, wherein the objectdepicted in the video includes at least one of a logo, a brand, or acommercial.